Generating Education in-Game Data: The Case of an Ancient

Theatre Serious Game

Nikolas Vidakis

1 a

, Anastasios Kristofer Barianos

, Apostolos Marios Trampas

Stamatios Papadakis

2 b

, Michail Kalogiannakis

2 c

and Kostas Vassilakis

Department of Informatics Engineering, Technological Educational Institute of Crete, Heraklion, Crete, Greece

Department of Preschool Education, Faculty of Education, University of Crete, Greece

Department of Electronic Engineering, Technological Educational Institute of Crete, Heraklion, Crete, Greece

stpapadakis@gmail.com, mkalogian@uoc.gr, kostas@cs.teicrete.gr

Keywords: Learning Analytics, Serious Games, Unity 3D, Experience API, Learning Record Store, ThimelEdu.

Abstract: Learning Analytics have become an indispensable element of education, as digital mediums are increasingly

used within formal and informal education. Integrating specifications for learning analytics in non-traditional

educational mediums, such as serious games, has not yet reached the level of development necessary to fulfil

their potential. Though much research has been conducted on the issue of managing and extracting value from

learning analytics, the importance of specifications, methods and decisions for the initial creation of such data

has been somewhat overlooked. To this end, we have developed a custom library that implements the

Experience API specification within the Unity 3D game engine. In this paper, we present this library, as well

as a representative scenario illustrating the procedure of generating and recording data. Through this work we

aim to expand the reach of learning analytics into serious games, facilitate the generation of such data in

commercially popular development tools and identify significant events, with educational value, to be

recorded.

1 INTRODUCTION

The rapid growth of ICT during the past decades and

the resulting significant lifestyle changes have not left

education and learning unchanged. Students, in our

digital age have developed different thought

processes, compared to students of the past that did

not use digital mediums, and thus education has to

transform in order to adapt (Prensky, 2001).

Additionally, this development and integration of

ICTs in our lives has also raised new demands in

education (Kalogiannakis and Papadakis, 2007;

Kalogiannakis, 2008). Ongoing research in the field,

aims to create innovations that will address students’

current and future needs (Livingstone, 2012). As a

result, a number of digital tools, devices and

platforms are now available to education

professionals. They can therefore rethink and reshape

educational and assessment practices, methods and

a https://orcid.org/0000-0003-0726-8627

b https://orcid.org/0000-0003-3184-1147

c https://orcid.org/0000-0002-9124-2245

environments to be the best fit for their students, with

less ties to traditional teaching and grading methods

and practices (McFarlane et al., 2002). Such digital

resources include interactive media, learning

management systems (LMSs), massive open online

courses (MOOCs), distance learning and e-learning

platforms, blended learning, educational video

games, learning analytics etc.

Educators, convinced of the benefits new

technologies carry, are actively exploring

technologies and methodologies, though often

hindered by factors such as technological

acclimatization (Kalogiannakis and Papadakis, 2007;

Kalogiannakis, 2008). Utilization of such resources,

along with innovative thinking have brought changes

to education, creating learning opportunities through

activities that would not traditionally be considered

educational, such as the usage of robotics to learn

math and sciences or the utilization of gaming for the

Vidakis, N., Barianos, A., Trampas, A., Papadakis, S., Kalogiannakis, M. and Vassilakis, K.

Generating Education in-Game Data: The Case of an Ancient Theatre Serious Game.

DOI: 10.5220/0007810800360043

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 36-43

ISBN: 978-989-758-367-4

development of social, personal and creative skills

(Vidakis et al., 2015; 2014).

Traditional assessment with explicit grading

systems are still the most prominent method used by

educators, as it is thought of as robust. However, this

model is considered to be problematic as it fails to

reflect accurate information regarding the overall

learning process and instead it is focusing on very

firm subsections. To overcome these impediments, a

more holistic appraisal of learners must be explored,

as a more trustworthy system that bares more

similarities with realistic situation (Sadler, 2009).

Additionally, active collection of learning data,

through behaviour and interaction during learning

activities, and the resulting analytics, offer the

potential of gaining actionable insights (Arnold,

2010; Elias, 2011). The assessment and grading

system that can emerge could facilitate a shift in focus

of observation, from quizzes and scores to a more

holistic and subjective approach.

Furthermore, learning analytics can be a powerful

addition to the educators’ toolbox of digital and

online learning, not only for evaluation, but also for

improvement of processes and to bring forth the best

in every student. In this paper we work with the

creation of raw data for learning analytics in the

context of a serious game and the design and

implementation of a software library that allows the

recording of learning analytics in a serious game

developed with the Unity 3D engine.

2 BACKGROUND

2.1 Educational Data

Learning has evolved to a state where personalized,

learner-centred experiences are created (Shen et al.,

2009). This shift in the educational balance has led to

a research interest in the subject of data mining within

education. Data collected from historical or

operational sources from various educational

institutes (dos Santos Machado and Becker, 2003; El-

Halees, 2009; Mostow and Beck, 2006) has research

value aiming to understand and boost student

performance, to assist educators and improve the

educational processes in general (Romero and

Ventura, 2007).

All this vast amount of educational data that

becomes available needs to be analysed and

processed so that conclusions can be drawn and

learning tools can be enhanced. Furthermore, failure

to utilize such data within digital learning processes

makes assessment a difficult task, comprised by many

layers (Béres et al., 2012). This brings extra obstacles

in blended learning settings. Educators must handle

assessment in many forms, including physical and

digital data at the same time. Recording data during

the learning and evaluation process can offer multiple

benefits. By utilizing complementing tools for

processing and analysing the generated data, valuable

conclusions can be drawn.

Thus, simplifying the procedure, accelerating the

grading process (Gaur, 2015) and offering insights

that educators could possibly overlook during manual

handling of all the aforementioned data. Digital

educational environments that enclose usage data can

be met in official educational institutions such as

schools and universities, and unofficial learning

settings such as digital educational games. This

fragmentation of data is desirable as learning is not

confined to a single physical or digital location and it

is distributed and diversified through numerous

locations and many forms (Vidakis et al., 2014).

Through learning analytics, educators and teachers

can predict students’ performance in the educational

process. Educators, by analysing previous data of

learners’ behaviour, can foresee and develop a clear

view of learners’ progress in the learning

environment. For instance, teachers can predict if a

student will pass or fail a course, or if there is a need

for additional assistance (Drachen et al., 2013;

Prensky, 2001).

Furthermore, in case that a challenge or exercise

is too difficult for some learners, educators, assisted

by learning analytics, have the opportunity (a) to

identify the difficulty and (b) to make necessary

changes to the learning process to meet learners needs

and thus create a personalized educational process,

which meets student’s needs. According to the above,

fewer students will fail or abandon the learning

process (Drachen et al., 2013; Prensky, 2001).

As a result, learning analytics are not only useful

for tracking and observing learners in a learning

environment. Instead, they offer groundwork for

educators to have facts to accurately plan their

teaching process and thus to transform the learning

environment and the relationships within the

classroom, by creating favourable conditions and

motivating students (Kalogiannakis and Papadakis,

2007; Kalogiannakis, 2008; Papadakis, 2018). The

utmost goal is a powerful, live and adaptable

educational procedure with main purpose to educate

and train. Digital educational environments can thus

act as a learning setting where raw data for learning

analytics is generated.

Generating Education in-Game Data: The Case of an Ancient Theatre Serious Game

2.1.1 Serious Games Generated Data

Educational or serious games belong to the category

of interactive games that have as main objective to

educate or train and as secondary objective to

entertain (Makarius, 2017). Through the gameplay,

players can reach new or enhance existing knowledge

on a specific topic (McFarlane et al., 2002; Vidakis et

al., 2015). Moreover, serious games improve the

player’s awareness on a specific topic and help to

change its attitude and perspective of this topic.

During gameplay each game has the potential to

create information, in the form of raw usage data,

about player’s behaviour inside the game. This

information can be thought as traces of the learner,

that mirror how he seeks, acquires and comprehends

knowledge. Different serious games may record

different data, like questions, interactions and

completion in order to calculate the learning process

outcomes.

We can classify those data as static or dynamic.

Static data are not changing overtime, while dynamic

do (Serrano-Laguna et al., 2017). Based on the above,

standards and specifications were created concerning

the creations of raw data from serious games

(Makarius, 2017).

2.2 Learning Analytics

Learning analytics can be thought of as systems with

a set of operations to store and analyse usage data and

create valuable information about students and the

learning process (Vidakis et al., 2014). Furthermore,

learning analytics can be a collection of data relating

to learner’s behaviour in a learning environment. The

development of learning analytics is used in

educational environments for measuring, collecting,

analysing, recording and visualizing progress,

behaviour and interactions within a learning

environment.

By creating learning analytics and studying their

results it is possible to enhance and create more

effective learning environments (Drachen et al.,

2013; Prensky, 2001). To effectively utilize learning

analytics, it is crucial that both learners and educators

use the outcomes to improve all aspects of the

learning procedure (Arnold, 2010). Learning

Analytics’ elements can be utilized to collect, store

and process educational data. In a serious game

setting those elements would specifically be

represented by a learning analytics component.

Traces of player’s interactions in the game are being

recorded in real time, without affecting the game’s

flow and in various formats, such as relational

databases or structured files.

Future analysis and processing of this data can

lead to valuable conclusions regarding the learner, the

learning process, and the assessment form which, in

turn, can be used to optimize the learning experience.

Raw data for learning analytics can be recorded with

three different types of triggers (Drachen et al., 2013;

Prensky, 2001): (a) Event based: Data are tied to in-

game events, (b) State based: Game’s information is

recorded at a specific frequency, rather than at a

specific event, and (c) Initiated events: Recording

data depends on data that are switchable, i.e. can be

enabled or disabled.

2.2.1 Learning Analytics Frameworks

SCORM: SCORM specification (Advanced

Distributed Learning, 2009) is a standard in the

development of educational resources, it has been

used to create learning analytics and it was introduced

by ADL (Advanced Distributed Learning Initiative,

n.d.) in 1999. With SCORM specification it is

possible to record learner’s progress, answers to

questions, interactions, completion and success

status. Furthermore, it is possible to record

interactions, objectives and goals in the context of a

serious game. However, there is a huge drawback in

the SCORM specification. It is designed to record

results and thus although data like interactions can be

captured, they cannot be analysed (Drachen et al.,

2013; Lukarov et al., 2014).

Activity Streams: Activity Streams specification

(W3C, 2017) was developed to solve SCORM’s

drawback. In this specification actions are being

represented as activities, users as actors and actions

as verbs. Composing this triple, it is possible to record

“who did what” in the context of a learning

environment or an educational game. This

specification was initially applied to record user’s

interactions in social networks. IMS Caliper and ADL

Experience API (xAPI) were based on this

specification (Lukarov et al., 2014; Serrano-Laguna

et al., 2017).

IMS Caliper: IMS Specification of Learning

Measurement for Analytics (IMS Caliper) (Romero

and Ventura, 2007), is a comprehensive specification

for learning measurement created by IMS and it was

developed by the IMS Global Consortium. Preceding

specifications act as the IMS Caliper’s core, making

the specification robust and offering levels of

maturity. This also offers backwards and future

compatibility for data and statistics. As a framework,

it provides abstract recommendations on the process

CSEDU 2019 - 11th International Conference on Computer Supported Education

of gathering and sharing learning data (Avila et al.,

2016). The main logic behind Caliper is the event

triggered data triples, which consist of (a) the event,

(b) an action, actor or object and (c) the activity

context. Those triples are grouped into Metric

Profiles that model activities. Transportation and

communication of data is achieved through the

Sensor API, handling documents in JSON-LD form

(“Caliper Analytics Specification,” n.d.).

Additionally, Caliper describes the services and

operations that the Learning Record Store will

handle.

Experience API: The Experience API (Rustici

Software, n.d.) is based on the Activity Streams’

specification and was developed as an open source

API. The xAPI was created for the purpose of

capturing activities and recording learning analytics

within different learning environments. The xAPI,

defines each learning activity as a statement. The

statement’s format is based on Activity Streams’

philosophy of “who did what”. Statements consisted

of actors (users), verbs (actions) and activities

(objects). Each statement can include additional

information like the resulting outcome of an activity,

a timestamp, an API’s version and many more

(Drachen et al., 2013; Rustici Software, n.d.).

Experience API saves statements in a Learning

Record Store (LRS). LRS is a data record store, where

recorded statements are being saved in sequential

order. “The LRS is the heart of any Experience API

ecosystem” (Ferguson, 2012; Rustici Software, n.d.).

It is responsible for receiving, storing and retrieving

learning data about learner’s activities, interactions

and experiences. Different LRSs can communicate

and exchange data with each other. The xAPI’s main

advantage, as ADL argues, is the freedom that

provides in respect to the statements, history, device

and workflow.

Experience API provides a huge vocabulary of

verbs and activities. Statements follow the

philosophy of “who did what” and xAPI is

characterized by its cross compatibility throughout

different devices. Based on the above and according

to the requirements of our system, that are elaborated

at the next sections, we chose to base our design and

implementation on the Experience API (xAPI)

specification.

3 OUR APPROACH

Our goal is to design and develop a software library

that can be used by the Unity3D game engine, to log

the activity during a gameplay session. We focus on

raw learning data of educational games; however, the

library can be used by any game created with

Unity3D. Our aim is to create an infrastructural

component, as presented in Figure 1, with cross-

platform compatibility to create and store in-game

activity data into a Learning Record Store (LRS). The

current implementation of our library is developed

with the C# programming language, under the Mono

framework, and is based on the stock .NET

implementation for xAPI (Rustici Software, 2015).

Therefore, it is not compatible with other game

engines. Nevertheless, our architecture can be

adapted to serve other game engines.

3.1 Ecosystem Architecture

The architecture of our library, that allows the Unity

3D game engine to create gameplay data, is based on

a layered modules approach (see Figure 1).

Specifically, the design architecture is based on four

modules. The first layered module refers to the

different devices and platforms that the user can use

to interact with the game, such as smartphones,

desktop PCs, consoles and tablets (see upper left

corner of Figure 1). The next layered module involves

the game constituents and the associated user roles

(see lower left corner of Figure 1). The third layered

module is the core of our library (see centre of Figure

1) with all the necessary procedures to create and

record gameplay data, during play. The fourth layered

module (see upper right corner Figure 1) incorporates

the data types and the dataspace where all the

gameplay data are stored as dictated by the xAPI

framework and the LRS that is in use. Using a device

of any kind, a tablet for instance, a game is being

played by a user. This triggers our library to create

gameplay data and store these data in an LRS. Then

these data can be used to create learning analytics, i.e.

statistics and visualizations.

Figure 1: Ecosystem Architecture.

3.2 Unity xAPI Library Architecture

The design of our xAPI library for Unity3D, as

presented in Figure 2, consists of five basic

components. Those components combine and realise

Generating Education in-Game Data: The Case of an Ancient Theatre Serious Game

Figure 2: Unity xAPI Library Architecture.

the efforts to parse JSON strings and files, generate

xAPI Statements, connect and send the data to the

Learning Record Store.

The statements are xAPI entities, represented by

the triples but carrying a semantic value with actors,

verbs, etc. This JSON structure is illustrated in

Exhibit 1. These statements must be prepared for

every recordable action within a specific game and

then they are dynamically created at playtime, with an

event-triggered mechanism.

The LRS that will be used is also predefined, and

upon each event that generates a statement, a

connection to this LRS is established and the data is

sent in JSON format.

4 REPRESENTATIVE

SCENARIO: ThimelEdu

To illustrate some of the concepts described so far and

to provide insight in our xAPI library for Unity 3D,

we modified the game ThimelEdu (Vidakis et al.,

2018), adding our library and thus support for xAPI.

A Learning Locker (HT2 Labs, n.d.) server was also

set up to serve the LRS for the system. Additionally,

we describe a representative scenario emphasizing

the data being recorded at play time (see Exhibit 2).

According to our scenario at Exhibit 2, as the

lesson starts, learner Paul must login to ThimelEdu

and start navigating inside the virtual world of an

ancient theatre. This activates several parallel

activities through the gameplay. Our collaborative

xAPI library for Unity 3D records learner’s data in

specific triggered events inside the gameplay. These

parallel activities of the gameplay are being described

below as play steps. When Paul, logs in the game,

Figure 3 (i) (a), an event is triggered. According to

this game event our xAPI library for Unity3D creates

a statement (see Figure 3 (i) (b)), and through the

library the statement is sent to and saved in our LRS.

As the game proceeds and Paul moves around the 3D

virtual world of the ancient theatre, he reaches a point

of interest (see Figure 3(ii) (a)). Points of interest are

highlighted with a blue halo to make them visible to

the player. When user interacts with a point of interest

the user’s first name and last name and as well as the

interest point are saved in the LRS (see Figure 3(ii)

(b)). The result of Learner Pauls’ interaction with a

Exhibit 1: xAPI Statement represented in JSON format

{ "id": 4,

"description": "Statement if answer is correct",

"verb_id": "http://adlnet.gov/expapi/verbs/answered",

"verb_language": "en-US",

"verb_text": "answered",

"activity_id":

"http://activitystrea.ms/schema/1.0/question",

"activity_game": null,

"completion": true,

"success": true }

Exhibit 2: Paul is a student. His teacher in history, decided

to teach Greek ancient theatre through an interactive

educational game, called ThimelEdu. Through play Paul

will achieve appropriate knowledge via blended learning

processes such as study of multimedia material in 3d

worlds, discovery learning models and explorative learning

(Bruner, 2009). As the lesson starts, Paul must login to

ThimelEdu and start navigating inside the virtual 3D world

of an ancient theatre. By interacting with scattered points,

he can read material, study photos see videos, as well as

contemplate and reply to questions. The eduGame finishes

when Learner has interacted with all artifacts.

CSEDU 2019 - 11th International Conference on Computer Supported Education

Figure 3: Game Steps.

game artefact (interest point) is that all available

material concerning this specific interest point is

presented to him in the form of text, photos and video.

Paul is now able to browse through the available

material as seen in Figure 3(iii) (a). When Paul has

studied all material and presses the “OK” button a

xAPI statement is configured and saved in our LRS

in the form presented in Figure 3(iii) (b). Once the

studying material step is completed then the

answering question step is followed for those interest

points that include questions. Paul answers a question

as shown in Figure 3(iv) (a) and the xAPI statement

is adjusted and saved in the LRS, in the form

presented in Figure 3(iv) (b).

4.1 Learning Analytics Results

As Paul or any other player/learner, in distinct game

instances, wander around the 3D virtual word of

ThimelEdu and interact with the various artefacts of

the game the LRS is updated with statements in the

form of triples as shown in Figure 4. These learning

analytics statements hold information about the

players’ educational status, progress and learning

outcomes. These triples can be used by analytics tools

to visualize learner’s behaviour in the learning

environment which, in our case, is a serious game,

and thus, extract data and conclusions about student’s

learning experience. According to these conclusions

an educational expert can then intervene in the

learning process and adjust the context, the learning

or assessment process, the grading algorithm, or any

other educational elements to provide a better

educational experience with increased benefits for the

learner. Furthermore, through studying learning

analytics in general, educators can predict students’

performance and draw conclusions that will be used

to enhance the educational process according to each

student’s individual behaviour.

Figure 4: Saved Statements in LRS.

Educators can change the educational material

and content, such as readings, question etc. In

addition, serious game developers, based on

educators’ conclusions, can adjust games and change

gameplay, in order to improve educational tools and

Generating Education in-Game Data: The Case of an Ancient Theatre Serious Game

Figure 5: Learning Locker Dashboard.

change the existing teacher-centred learning process

philosophy.

Figure 5 shows a dashboard within Learning

Locker with indicative visualizations. Upper left

corner shows individual students and their successful

and failed attempts at answering questions. Bottom

left corner shows the overall attempts, during a

specified session. Upper right corner shows a pie

chart with the number of players that played the

game, quit the game without completion and those

who completed, over a period of a month. Because of

the way the pie is constructed, a rough ratio can also

be quickly estimated by the visual representation.

Finally, in the lower right corner a counter is showing

how many players have completed the game during

the specified game session.

5 CONCLUSIONS

According to the current research, learning analytics

is a powerful tool for monitoring a learner’s

behaviour in the new educational era. Its power can

assist educators in drawing important conclusions

about learner’s educational behaviour, process and

needs. Although analytics specifications were first

developed for social networks, they are now capable

to build the fundamentals of new and modern

pedagogical environments and therefore of a new e-

Learning world. The development of standards and

specifications through the years has made developers

able to monitor and record learner’s behaviour,

interactions and knowledge. Experience API is a

state-of-the-art specification for the development of

learning analytics. The plain structure of Experience

API along with the rich vocabulary of verbs and

activities, make the library an integral part of learning

analytics development. On the other hand, potential

obstacles for the learning analytics approach to

education with games can be (a) time demanding

nature of some games, (b) lack of updated and

expensive hardware in schools, (c) price of games and

(d) unfamiliar teachers with technology. Future

developments include the standardization of our

library and the development of a specification for

developers that want to use it in their games.

Moreover, we plan to run large scale experiments in

different classes with pupils of varying ages.

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